Pattern recognition techniques, such as clustering algorithms, are applied to
recordings of arterial distension waveforms to detect emergent properties of data.
The feature extraction stage is based on the Fast Fourier Transform components
analysis. Statistical K-means clustering helps in the feature selection step.To
generalize the method uses both neural network self-organizing feature mapping
and neural network supervised learning to classify waves according to patient
age. This process shows encouraging results for a set of blood pressure
recordings belonging to three differents decades.
1. Introduction
Illnesses characterized by a decrease of elasticity of arterial walls, such as
atheroclerosis